论文标题
信号到背景区分，以通过衰减通道中的向量玻色子融合机制生产双Higgs玻色子事件，并在LHC实验的最终状态下，带有四个带电的瘦素和两个B-JET
Signal to background discrimination for the production of double Higgs boson events via vector boson fusion mechanism in the decay channel with four charged leptons and two b-jets in the final state at the LHC experiment
论文作者
论文摘要
在CERN大型强生对撞机实验中，通过矢量 - 玻璃融合的非共振双HIGGS生产代表了一种探测VVHH（V = Z，W $^{\ pm} $）Higgs Higgs Higgs自耦合在当前大量能量中心的独特均值。这种罕见的信号不能通过应用一些基于剪切的选择来从巨大的背景中有效分离。实际上，在这项工作中，使用深度学习算法来决定事件是否更像信号或背景。特别是，我们报告了两个主要方面：超参数平行扫描策略的结果，以在RECAS-BARI数据中心计算资源的多个节点上分发训练过程以及深度神经网络体系结构的性能。
At the CERN Large Hadron Collider experiment, the non-resonant double Higgs production via vector-boson fusion represents a unique mean to probe the VVHH (V=Z, W$^{\pm}$) Higgs self-coupling at the current center of mass energies. Such a rare signal cannot be separated efficiently from huge backgrounds by applying a few-observables cut-based selection. Indeed, in this work, a Deep Learning algorithm is used to decide whether an event is more signal- or background-like. In particular, we report on two main aspects: results of a hyper-parameters parallel scanning strategy to distribute the training process across multiple nodes on the ReCaS-Bari data center computing resources and the discriminating performance of a Deep Neural Network architecture.
